Systems and methods for routing electronic transactions using predicted authorization approval

ABSTRACT

A method for routing electronic payment transactions includes receiving transaction-related information from a merchant, extracting transaction routing criteria from the received transaction-related information, dynamically identifying one or more eligible payment networks based on extracted transaction routing criteria, predicting a likelihood of authorization acceptance for each identified network based on the transaction-related information, dynamically identifying one or more breakeven transaction amounts for each identified eligible payment network, each breakeven transaction amount defining a point at which two or more eligible payment networks have the same expenses for a given transaction amount, the expenses including costs associated with a low predicted likelihood of authorization acceptance, and routing signature debit transactions from the merchant to a least cost PIN-less debit network selected from the eligible payment networks based on identification of a desired breakeven transaction amount for the PIN-less debit network.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application is a continuation of and claims the benefit ofpriority to U.S. application Ser. No. 16/855,574, filed on Apr. 22,2020, now U.S. Pat. No. 11,216,789, which is a continuation of andclaims the benefit of priority to U.S. application Ser. No. 15/839,185,filed on Dec. 12, 2017, now U.S. Pat. No. 10,706,393, the entireties ofwhich are incorporated herein by reference.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally to thefield of routing electronic payment transactions and, more particularly,to routing electronic payment transactions using payment pseudo-networksand electronic transaction simulation based on predicted authorizationapproval and forecasting.

BACKGROUND

In the world of payments, merchants, such as retailers and e-commercesites, may choose various acquiring institutions or banks (“acquirers”)to process payment transactions through the various payment networksused by consumers. The payment networks may include credit networks(e.g., Visa, Master Card, Discover, American Express, etc.) and/or debitnetworks (e.g., Star, Plus, Jeanie, Pulse, etc.). Consumer card issuersmay decide which groups and types of networks to accept, and merchantsmay further determine which processors and networks to routetransactions through.

Payment networks, in turn, may use a number of factors to determine theinterchange category and/or interchange rate for a given transaction.Some of these factors may be controlled or influenced by the merchant,the factors including but not limited to, the processing method (e.g.,card present and card-not-present), the Merchant Category Code (MCC),and transaction data. However, payment networks may also use factorsthat may be outside of the control of a merchant to determine theinterchange category and/or interchange rate for a given transaction.These factors, which a merchant may not be able to control or influenceinclude, but are not limited to, the card type (separate interchangecategories exist for credit and debit as well as corporate cards,prepaid cards, etc.), the card brand (Visa, MasterCard, etc.), and/orthe card owner (whether a credit or debit card is issued by a smallcredit union, regional bank or large National bank).

If a merchant has a large volume of transactions, then the savings frompaying the lowest transaction fees could easily add up to hundreds ofthousands of dollars per month. The ability to route transactions leastcost is further complicated by cases where a merchant has multiplelocations and/or multiple business lines per location in the case ofmulti-format retailer, such as a “big box store” (ex: photographysection, salon section, vision section, electronics section, apparelsection, etc., wherein each section may have its own MCC).

Furthermore, analyzing transaction costs and making routing decisionsmay be complicated by both (i) mandatory state and Federal regulatoryrules and (ii) voluntary agreements among issuers, networks, andprocessors, any of which may pertain to negotiated transactionvolume/amount thresholds, negotiated markup rates, exemption fromregulations, and preferences. As an example, under the “Durbinamendment” of the Dodd-Frank financial reform legislation of 2010,financial institutions having over $10B in assets may be considered“regulated,” whereas financial institutions having less than $10B inassets may be “exempt.” Moreover, many Debit Networks (e.g. Star,Jeanie, etc.) create “preferred rates” that may be different from“standard rates,” and these rates may change from merchant to merchant,and/or from issuer to issuer. As a result, when compiling a “ratesheet,” it can be important to know which merchants or issuers arepreferred, and what the preferred rates are. Many networks also chargenot only based on “standard” vs. “preferred,” but also regulated vs.exempt, and based on card type (prepaid, business, etc.) and transactionvolumes/amounts over time.

In addition, a failure to authorize a transaction may require aresubmission of the transaction and an increase in transaction costsassociated with the resubmission. Accordingly, an undeterminedprobability that a transaction will be declined may further complicatethe estimation of transaction costs across multiple networks.

Thus, while a static table of networks or issuers might provide someinitial insights into costs, the real costs may depend on regulatorystatus and/or whether certain regulatory or contractual thresholds(maximums or minimums) have been reached in some given time period.Since actual costs or rates may depend on total numbers of transactionsand a likelihood that a transaction will be declined, it can bedifficult to predict the real costs and/or the ideal routing for anygiven transaction.

Thus, there is a desire for a system and method for allowing merchantsto automatically find the most desired networks through which to routeelectronic payment transactions, on a dynamic and granular level.

The present disclosure is directed to overcoming one or more of theseabove-referenced challenges.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the present disclosure, systems andmethods are disclosed for routing electronic payment transactions toPIN-less networks using payment pseudo-networks and electronictransaction simulation using predicted authorization approval.

In one embodiment, a computer-implemented method is disclosed forrouting electronic payment transactions to PIN-less networks usingpayment pseudo-networks and electronic transaction simulation usingpredicted authorization approval, the method comprising: receivingtransaction-related information from a merchant, the transaction-relatedinformation including a bank identification number (“BIN”), one or moreavailable payment network IDs, one or more merchant categories, anissuer regulatory status, a transaction amount, and a preferred status,extracting transaction routing criteria from the receivedtransaction-related information, dynamically identifying one or moreeligible payment networks based on extracted transaction routingcriteria, predicting a likelihood of authorization acceptance for eachidentified network based on the transaction-related information,dynamically identifying one or more breakeven transaction amounts foreach identified eligible payment network, each breakeven transactionamount defining a point at which two or more eligible payment networkshave the same expenses for a given transaction amount, the expensesincluding costs associated with a low predicted likelihood ofauthorization acceptance, and routing signature debit transactions fromthe merchant to a least cost PIN-less debit network selected from theeligible payment networks based on identification of a desired breakeventransaction amount for the PIN-less debit network.

In accordance with another embodiment, a system is disclosed for routingelectronic payment transactions to PIN-less networks using paymentpseudo-networks and electronic transaction simulation using predictedauthorization approval, the system comprising: a data storage devicestoring instructions for routing electronic payment transactions toPIN-less networks using payment pseudo-networks and electronictransaction simulation in an electronic storage medium; and a processorconfigured to execute the instructions to perform a method including:receiving transaction-related information from a merchant, thetransaction-related information including a bank identification number(“BIN”), one or more available payment network IDs, one or more merchantcategories, an issuer regulatory status, a transaction amount, and apreferred status. extracting routing criteria from the receivedtransaction-related information, dynamically identifying one or moreeligible payment networks based on extracted transaction routingcriteria, predicting a likelihood of authorization acceptance for eachidentified network based on the transaction-related information,dynamically identifying one or more breakeven transaction amounts foreach identified eligible network, each breakeven transaction amountdefining a point at which two or more eligible networks have the sameexpenses for a given transaction amount, the expenses including costsassociated with a low predicted likelihood of authorization acceptanceand routing signature debit transactions from the merchant to a leastcost PIN-less network selected from the eligible networks based onidentification of a desired breakeven transaction amount for thePIN-less debit network.

In accordance with another embodiment, a non-transitory machine-readablemedium storing instructions that, when executed by the a computingsystem, causes the computing system to perform a method for routingelectronic payment transactions to PIN-less networks using paymentpseudo-networks and electronic transaction simulation using predictedauthorization approval, the method including: receivingtransaction-related information from a merchant, the transaction-relatedinformation including a bank identification number (“BIN”), one or moreavailable payment network IDs, one or more merchant categories, anissuer regulatory status, a transaction amount, and a preferred status,extracting routing criteria from the received transaction-relatedinformation, dynamically identifying one or more eligible paymentnetworks based on extracted transaction routing criteria, predicting alikelihood of authorization acceptance for each identified network basedon the transaction-related information, dynamically identifying one ormore breakeven transaction amounts for each identified eligible network,each breakeven transaction amount defining a point at which two or moreeligible networks have the same expenses for a given transaction amount,the expenses including costs associated with a low predicted likelihoodof authorization acceptance, and routing signature debit transactionsfrom the merchant to a least cost PIN-less network selected from theeligible networks based on identification of a desired breakeventransaction amount for the PIN-less debit network.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. As will beapparent from the embodiments below, an advantage to the disclosedsystems and methods is that multiple parties may fully utilize theirdata without allowing others to have direct access to raw data. Thedisclosed systems and methods discussed below may allow advertisers tounderstand users' online behaviors through the indirect use of raw dataand may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts a block diagram of an example environment and systems forrouting electronic payment transactions, in accordance with non-limitingembodiments.

FIG. 2A depicts a flow diagram of an exemplary method executed by atransaction routing server for routing electronic payment transactionsto PIN-less debit networks based on dynamic routing and predictedauthorization approval, in accordance with non-limiting embodiments.

FIG. 2B depicts a flow diagram of an exemplary method executed by atransaction routing server for routing electronic payment transactionsusing payment pseudo-networks and electronic transaction simulation andforecasting using predicted authorization approval, in accordance withnon-limiting embodiments.

FIG. 3 depicts a plurality of modules and/or sub-systems of thetransaction routing server of FIG. 1 , for routing electronic paymenttransactions to PIN-less debit networks and dynamically routingelectronic payment transactions using payment pseudo-networks andelectronic transaction simulation using predicted authorizationapproval, in accordance with non-limiting embodiments.

FIG. 4A depicts a table of payment networks including createdpseudo-networks, in accordance with non-limiting embodiments.

FIG. 4B depicts a table of payment networks including createdpseudo-networks, sorted according to a simulated forecast of transactionvolume and rates, in accordance with non-limiting embodiments.

FIG. 5 depicts a screenshot of an exemplary user interface fordisplaying results of routing electronic payment transactions usingpayment pseudo-networks and electronic transaction simulation usingpredicted authorization approval, and transaction forecasting anditeration.

FIGS. 6A and 6B depict a report of an anonymized PIN-less debit systemincluding a report of value drivers of savings (FIG. 6A) and a report ofPIN-less network volume shift (FIG. 6B).

FIG. 7 is a screenshot of an exemplary graphical user interface (GUI)depicting routing and settlement results for an exemplary merchant.

FIG. 8 is a screenshot of an exemplary graphical user interface (GUI)depicting an exemplary issuer distribution of issuer, brand, and networkdetail, with selective user elements enabling sorting.

FIG. 9 is a screenshot of an exemplary graphical user interface (GUI)depicting an exemplary interface for reviewing and selecting analternate network eligibility.

DETAILED DESCRIPTION OF EMBODIMENTS

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and use of systems and methods disclosed herein forrouting electronic payment transactions using payment pseudo-networksand electronic transaction simulation.

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

As described above, in some cases, analyzing transaction costs andmaking routing decisions may be complicated by (i) mandatory regulatoryrules, (ii) voluntary agreements among issuers, networks, andprocessors, any of which may pertain to transaction volume, markuprates, exemption from regulations, and preferences, and (iii) aprobability that a transaction will be declined. As an example,financial institutions having over $10B in assets may be considered“regulated” under Durbin, whereas financial institutions having lessthan $10B in assets may be “exempt.” Moreover, many processors create“preferred rates” that may be different from “standard rates,” and theserates may change from merchant to merchant, and/or from issuer toissuer. As a result, when compiling a “rate sheet,” it can be importantto know which merchants or issuers are preferred, and what the preferredrates are. Many networks also change not only based on “standard” vs.“preferred,” but also regulated vs. exempt, and based on card type(prepaid, business, etc.).

Thus, while a static table of networks or issuers might provide someinitial insights into costs, the real costs may depend on regulatorystatus and/or whether certain regulatory or contractual thresholds(maximums or minimums) have been reached in some given time period.Since actual costs or rates may depend on total numbers of transactions,it can be difficult to predict the real costs and/or the ideal routingfor any given transaction.

In addition, a failure to authorize a transaction may require aresubmission of the transaction and an increase in transaction costsassociated with the resubmission. Accordingly, an undeterminedprobability that a transaction will be declined may further complicatethe estimation of transaction costs across multiple networks.

In view of the foregoing, the present disclosure describes embodimentsof a transaction routing server configured to generate “pseudo-networks”designed to be compared against networks when performing decisioningwithin a rate sheet of networks. “Pseudo-networks” may be artificialnetworks or modified versions of networks and configured to simulaterouting options within the payments environment. Specifically, thedisclosed embodiments involve generating pseudo-networks mimickingactual payment networks, and generating and updating routing tablesreflecting forecasted routing transaction costs to ensure desiredtransaction volumes are being achieved while minimizing acceptancecosts. The present disclosure also describes embodiments of atransaction routing server configured to perform simulation andforecasting of transaction routing. For example, the disclosedembodiments also involve simulating and forecasting transactions for thepurpose of comparing data against historical data, and forecastingvolume against negotiated thresholds.

Thus, the present disclosure is directed to a proprietary, comprehensivesolution for routing electronic payment transactions using paymentpseudo-networks and electronic transaction simulation. Moreover, theembodiments of the present disclosure enhance transaction routingintelligence and reduce the cost of acceptance.

As will be described in more detail below, the presently disclosedsystems and methods may route and optimize transactions according to oneor more of the following factors: a determined probability that atransaction will be declined, merchant category code (MCC), regulatory(e.g., Durbin) qualification, transaction amount, full acceptance cost(e.g., I/C, switch, other, etc.), identification of standard/premierissuer status, identification of business vs. prepaid cards, BIN/networkidentification, and/or interchange monitoring/forecasting.

The disclosed embodiments are relevant to any type of credit and/ordebit transactions, including both PIN and PIN-less, and are designed toreduce expenses while also optimizing across various dimensionsaccording to various desires. As disclosed herein, the presenttechniques also include electronic displays for purposes of real-timereporting, monthly reporting, annual reporting, and the like, forreflecting to clients the savings resulting from the presently disclosedrouting techniques. The disclosed routing techniques also involve “PINprompting,” which reduces acceptance costs by steering consumers awayfrom signature transactions to PIN debit transactions, and seamlesslyrouting signature debit transactions to least-cost PIN-less debitnetworks.

As described above, the present disclosure is directed to both PIN andPIN-less transactions that reach a processor upon swiping, dipping, EMV,etc. initiation of a payment transaction. It should be appreciated thata payment processor may route each transaction to any of a number ofdifferent networks including Interlink (VISA), Maestro (MC), Pulse(Disc), Star (First Data), Accel, etc. In many cases, as a transactionis received, the processor may receive the primary account number(“PAN”), time/date stamp, amount, MCC, and determine the issuer byanalyzing the received PAN and determined which network or networks areenabled by the given issuer (e.g., a given issuer may have enabled,e.g., Interlink, Star, and Accel). According to the embodiments of thepresent disclosure, additional routing analysis and decisioning may beperformed to determine, in real-time, the actual cost of a giventransaction, based on one or more factors or criteria. For example, atransaction routing server consistent with the disclosed embodiments mayreceive transactions and extract routing criteria comprising, categorycode, ticket amount, and so on. A transaction routing server consistentwith the disclosed embodiments may further determine a probability thata transaction will be declined when presented to one or more network ornetworks based on the parameters of the transaction.

The cost of fees charged by acquirers and networks for paymenttransactions may impose significant costs on merchants, especially forlarge volumes of transactions. It may also be burdensome or otherwiseimpossible, to date, for a merchant, to sign up for and, at everypayment transaction, search for, the least cost acquirer, network,and/or pricing model, or be able to manage the communication oftransaction information between payment terminals, acquirer processors,and networks, especially when there are different messaging formats usedby the payment terminals or networks.

Thus, the embodiments of the present disclosure are also directed tomethods and systems to identify and achieve the lowest cost for eachpurchase transaction initiated by a merchant through the creation of amarketplace model. The marketplace model may include a computing system,which may include a “transaction routing server” that selects, fromamong a marketplace of networks, a network that provides the “leastcost” (e.g., lowest cost) acceptance or mark-up rate. Furthermore,various embodiments of the present disclosure describe systems andmethods for enabling the transaction routing server to communicate andnetwork efficiently between various payment terminals, and a pluralityof networks.

One or more examples of these non-limiting embodiments are illustratedin the selected examples disclosed and described in detail withreference made to the figures in the accompanying drawings. Those ofordinary skill in the art will understand that systems and methodsspecifically described herein and illustrated in the accompanyingdrawings are non-limiting embodiments. The features illustrated ordescribed in connection with one non-limiting embodiment may be combinedwith the features of other non-limiting embodiments. Such modificationsand variations are intended to be included within the scope of thepresent disclosure.

FIG. 1 depicts a block diagram of an example environment and systems forrouting electronic payment transactions to PIN-less debit networks anddynamically routing electronic payment transactions using paymentpseudo-networks and electronic transaction simulation. It should beappreciated that the embodiments of the present disclosure, includingFIG. 1 , are also applicable to PIN debit and credit networks. At a highlevel, the transaction routing environment and systems (“system”) 100comprises: a payment vehicle 102 being used at a merchant 106 via aterminal 110A-C; a computing system (“transaction routing server” 116)that selects from among a marketplace of payment networks 122A-122C; aplurality of issuers 130A-130C; and a network 114 (e.g., the wired orwireless Internet, LAN, and/or PSTN) that may enable communicationbetween the various systems and entities. In some embodiments, certainrate qualification criteria 124A-124C, as determined by various paymentnetworks 122A-122C may be used to map an appropriate regulatory status132A-132C of an issuer 130A-130C to standard rates 126A-126C vs.preferred rates 128A-128C for a given issuer or merchant.

Still referring to FIG. 1 , the payment vehicle 102 may be a tangibleobject (e.g., a credit card, debit card, gift card, etc.), an electronicdevice (e.g., in the case of ApplePay, Samsung Pay, Bitcoin, or thelike), and/or an intangible representation of a user's payment sourcethat may be used to initiate a payment transaction at a payment terminal110A-110C of a merchant 106 by delivering information regarding theconsumer's payment source (e.g., payment network 1 ID 104A, paymentnetwork 2 ID 104B, . . . payment network N ID 104C, etc.). It is alsocontemplated that for some merchants (e.g., e-commerce merchants), theremay not be a physical terminal. In such embodiments, a merchant's servermay serve as a terminal and the server may or may not have and/or send aterminal identifier. The payment vehicle may carry information of apayment network (e.g., Visa, MasterCard, Discover, American Express,JCB, etc., for credit networks, and/or Star, Plus, Jeanie, Cirrus, etc.,for debit networks) using a payment network identification 104A-104C.The payment network identification may include one or more of a paymentcard number, an issuer identification number, a primary account number(PAN), a bank card number, and/or a bank identifier code of a paymentsource account. A consumer may initiate a payment transaction at aterminal, for example, by swiping a card and/or otherwise facilitatingthe transmission of payment network identification (e.g.,electronically, verbally, etc.).

Still referring to FIG. 1 , a merchant 106 may refer generally to anytype of retailer, service provider, or any other type of business thatis in networked communication with one or more issuers (e.g., Issuer 1130A, Issuer 2 130B, . . . Issuer N 130C, etc.) and may use the paymentprocessing services of the respective, acquirers, issuers, and/orunaffiliated processors/networks. Payment processing services mayinclude receiving and responding to authorization requests as well asfacilitating the settlement of funds associated with card-basedtransactions occurring at merchant 106. One or more terminals (e.g.,Terminal 1 110A, Terminal 2 110B, . . . Terminal N 110C, etc.) may bemapped to merchant 106. As is known in the art, each terminal 110A-110Cmay be generally unmodified or “stock” and simply facilitate thestandard transmission of transaction-related information to thecomputing system of an acquirer 130A-130C. In various embodiments of thepresent disclosure, the transaction routing server 116 may act or beperceived by the terminals as either an issuer or an acquirer processorcomputer system. Thus, each terminal 110A-C may facilitate thetransmission of transaction-related information to the transactionrouting server 116. The transaction-related information may comprise atransaction authorization request (“authorization request”), includingbut not limited to, a payment amount, a date, a time, a payment networkidentification (e.g., a primary account number and/or issueridentification number) as well as other types of identifying indicia(e.g., merchant identification 108, terminal identification 112A-C,etc.). The identifying indicia may vary based on the terminal 112A-C,the type of merchant 106, or the payment network being used at theterminal.

Still referring to FIG. 1 , the network 114 may serve as a means forcommunicating information across the various systems and entities of theelectronic transaction routing system and environment. For example, insome embodiments, the network may facilitate the transmission of paymenttransaction information as well as identifying information of themerchant, terminal, and payment network used, to the transaction routingserver via the cloud, e.g., the Internet, or any type of wired orwireless wide area network. Network 114 may facilitate the periodic orcontinual updating of the transaction routing server 116 on paymentnetwork interchange rates from various computing systems, as well as themarkup rates for various acquirers from the computing systems of therespective acquirer institutions.

Still referring to FIG. 1 , transaction routing server 116 may be acomputing system comprising at least one processor 118 and at least onedata storage device, e.g., database 120. In some embodiments, thetransaction routing server 116 may receive information from the merchant106 and/or terminals 110A-C, maintain a database 120 of storedinformation related to payment networks, issuers, regulatory status,rate qualification criteria, etc., periodically or continually updateits database 120, and transmit information back to merchant 106 and/orterminals 110A-C. Database 120 may further include a dataset ofprocessing results that may be used to determine a likelihood ofauthorization or conversion for subsequent transaction requests. Uponthe initiation of a payment transaction at a terminal 110A-C, thetransaction routing server 116 may receive various transaction relatedinformation, which may include, but is not limited to, a BIN,identifications of available payment networks useable in the transaction(“payment network IDs”), merchant categories, regulatory status,transaction amount, “preferred” or “premier” status, etc. In someembodiments, the payment network identification may include a paymentcard number, whose first six digits may identify an issuer and/or bankinstitution that is associated with a payment network.

Still referring to FIG. 1 , upon receiving the transaction-relatedinformation, the transaction routing server 116 may use the extractedpayment network identification to determine which payment networks atransaction may be used (e.g., payment network 1 122A, payment network 2122B, payment network 3 122C). Depending on the payment networksavailable, transaction routing server 116 may subsequently use thatpayment network's rate qualification criteria 124A-124C to determine thestandard rates 126A-126C vs. preferred rates 128A-128C for thetransaction. In some embodiments, transaction routing server 116 mayalso use the rate qualification criteria 124A-124C to determineinformation about the relevant issuer, such as a regulatory status 132A132C. In some embodiments, the regulatory status is determined by theidentity of the issuer (e.g., more or less than $10B in assets), whereasthe standard vs. preferred rate is determined by the payment network.

In addition, the transaction routing server 116 may process the datasetof processing results to determine success factors related to alikelihood of authorization or conversion for subsequent transactionrequests. The success factors may be based on a status of a financialaccount associated with the transaction or cardholder, or other factors,such as, for example, the presence or absence of a billing address, thepresence or absence of a card verification value (CVV), the presence orabsence of a payment vehicle expiration date, the presence or absence ofa payment vehicle issuer token, a merchant classification code (MCC),etc. Generating the factor success data may include applying datascience methods, machine learning, or other suitable methods to thedataset of processing results. The generated factor success data may bespecific to each payment network 122 such that transaction routingserver 116 may compare the likelihood of authorization across multiplenetworks.

Thus, transaction routing server 116 is configured to evaluate andselect, from one of many networks (e.g., Payment Network 1 122A, PaymentNetwork 2 122B, . . . Payment Network N 122C, etc.), a payment networkthat may yield the least cost markup rate for a given transaction. Insome embodiments, this selection may include comparing the markup rateswithin various pricing models for each of the acquirers, selecting thepricing model yielding the lowest markup rate, for each of theacquirers, identifying networks with the lowest rates for a givenstandard vs. preferred status, and a given regulatory exemption status,and then selecting the lowest interchange, “acceptance,” and/or “markup”rate among all of the networks.

In addition, transaction routing server 116 may determine the likelihoodthat a transaction will be declined by each payment network. Theadditional costs of a declined transaction, including for example,additional fees for re-submitting the declined transaction, may also beincluded in the determination of the least cost network among the maynetworks (e.g., Payment Network 1 122A, Payment Network 2 122B, . . .Payment Network N 122C, etc.).

Still referring to FIG. 1 , in summary, once transaction routing server116 receives the transaction-related information from a terminal 110A-Cvia network 114, the transaction routing server 116 may retrieve, fromits database 120, the available rate information based on the ratequalification criteria of the payment networks available to be used inthe transaction. For example, if the transaction routing serveridentifies, based on the issuer ID and/or payment network ID provided inthe received transaction-related information that payment network 1 122Ais being utilized, transaction routing server 116 may retrieve, from itsdatabase 120, a list of eligible or available alternative paymentnetworks, their respective rate qualification criteria, their standardvs. preferred rates, and the issuer's regulatory status as either“exempt” or “regulated.” Transaction routing server 116 may alsodetermine the likelihood that a transaction will be declined by eachpayment network and the additional costs of a declined transaction.Subsequently, transaction routing server 116 may determine, from one ofmany networks 122A-122C, the network that provides the overall bestsolution for the merchant, whether or not that network is actually theleast costly for any given transaction. In some embodiments, the markuprates for the various networks and merchants may be stored withindatabase 120 of transaction routing server 116. The database 120,including the dataset of processing results, may be continually and/orperiodically updated by computing systems or servers representing theone or more issuers 130A-130C.

Still referring to FIG. 1 , once transaction routing server 116determines a matrix of various markup rates across issuers and networks,as well as a likelihood of acceptance of the transaction and the costsof a declined transaction across issuers and networks, transactionrouting server 116 may determine the total rate that would be incurredby the merchant for the transaction. Typically, the markup rate and/orthe acceptance rate may be determined to be one or more of a percentageof the transaction amount, a flat charge, or a value amount added to apercentage of the transaction amount. The likelihood of acceptance maybe based on a status of a financial account associated with thetransaction or cardholder, or other factors, such as, for example, thepresence or absence of a billing address, the presence or absence of acard verification value (CVV), the presence or absence of a paymentvehicle expiration date, the presence or absence of a payment vehicleissuer token, a merchant classification code (MCC), etc. Once theacceptance rate, costs of declined transactions, and the least costmarkup rate has been identified (e.g., from the decision-making processdepicted in FIGS. 2A and/or 2B), the rates may be combined and/or may besent along with the transaction-related information to the selectedissuer or network with the least cost markup rate for further processingof the payment transaction. In some embodiments, the combined ratesalong with information related to or identifying the selected networkmay be sent back to the payment terminal of the merchant or a computingsystem of the merchant. In other embodiments, after the combined ratesalong with transaction-related information has been sent to the selectednetwork with the least cost rate and the payment transaction has beenfurther processed, the acquirer may send information (“transactionprocessing acknowledgment information”) acknowledging the processing ofthe transaction back to transaction routing server 116. In suchembodiments, transaction routing server 116 may communicate thetransaction processing acknowledgment information to merchant 106 and/orpayment terminal 110A-110C of the merchant.

Since the various payment terminals and servers associated with theplurality of merchants, acquirers, acquirer processors, and/or paymentnetworks, with which the marketplace transmits and/or receivesinformation, may use different messaging formats, it is envisioned thatin various embodiments of the present disclosure, the transactionrouting server 116 has the ability to translate between and/or supportplatforms of various messaging formats. For example, if a paymentterminal communicates transaction related information in JSON butacquirer 1 communicates information regarding the transaction in XML,the transaction routing server may receive the information regarding thetransaction from acquirer 1 in XML, translate the information to JSON,and deliver the information to the payment terminal in JSON. In someembodiments, the task of translating messages of various formats into aformat readable by the recipient device (e.g., terminal) may beperformed by processor 118 of transaction routing server 116.

FIG. 2A depicts a flow diagram of an exemplary method executed by atransaction routing server for routing electronic payment transactionsto PIN-less debit networks based on dynamic routing, in accordance withnon-limiting embodiments. Specifically, FIG. 2A depicts a method ofrouting a transaction over a PIN-debit network even if a PIN is notpresent, by leveraging a relationship with the issuer. Such techniquesmay be performed by adding “signature transactions” as separate networksin the sequencing of available networks and pseudo-networks. Forexample, a decision may be made on whether to send a given transactionthrough credit networks or through PIN debit rails.

Specifically, FIG. 2A depicts a method 200 for routing electronicpayment transactions to PIN-less networks using payment pseudo-networksand electronic transaction simulation. In one embodiment, method 200includes step 205, which may include receiving transaction-relatedinformation from a terminal. As illustrated in steps 210A-210F, thetransaction-related information may include an issuing or “issuer” bankidentification number (“BIN”) 210A, an identification of the availablepayment networks (“payment network IDs”) 210B, an identification of oneor more merchant categories 210C, an identification of a regulatorystatus of the issuer (issuing bank) 210D, the transaction amount chargedor to be charged 210E, and a preferred (or non-preferred) status 210Fassociated with the merchant affiliated with the transaction. In someembodiments, for example in transactions involving e-commerce, or anonline purchase, the transaction-related information need not include aterminal ID, e.g., where a physical terminal does not exist. The modesand/or forms of payment may include, but are not limited to, the type ofcard presented, the specific information contained in the transaction,how and when a payment transaction is processed, the industry of themerchant, whether additional services (e.g., address verificationservice (AVS)) are utilized, etc.

Step 215 may include extracting routing criteria from thetransaction-related information, including but not limited to the BIN210A, available payment network IDs 210B, merchant categories 210C,issuer regulatory status 210D, transaction amount 210E, and preferredstatus 210F. In some embodiments, a default or initial payment networkmay be identified from the first digit of the payment card number and/ora bank card number (e.g., Visa, MasterCard, Discover, American Express,JCB, etc., for credit networks, and/or Star, Plus, Genie, Cirrus, etc.,for debit networks).

Step 220 may include dynamically identifying eligible networks based onextracted transaction routing criteria. For example, step 220 mayinclude identifying eligible networks based on the identity of thecardholder's issuing bank and/or the identity and/or category of therelevant merchant corresponding to the transaction.

Step 222 may include predicting a likelihood of authorization acceptancefor each identified network. For example, step 222 may include comparingone or more parameters of the payment transaction to the factor successdata. Such comparison may include applying factor weights from thefactor success data to the parameters of the transaction. Such factorweights may be determined by any suitable method, including, forexample, statistical methods including regression analysis, machinelearning, artificial intelligence methods such as neural networks, etc.

Step 225 may include dynamically identifying one or more breakeventransaction amounts for which each eligible network. In someembodiments, the breakeven point may define a point at which two or moreeligible networks have the same expenses for a given transaction, theexpenses including costs associated with a low predicted likelihood ofauthorization acceptance.

Step 230 may include routing signature debit transactions to a leastcost PIN-less network. In some embodiments, signature debit transactionsmay be converted and routed to a least cost PIN-less network based onidentification of a desired breakeven transaction amount for thePIN-less debit network. For example, in one embodiment, step 230 mayinvolve routing signature debit transaction to PIN-less networks byleveraging a processor's relationship with a given network, or between amerchant and a network. Specifically, eligible transactions may bedetermined based on BIN and organization ID. For example, a particularBIN may be used for PIN-less network eligibility (accounting for largepercentages of total network volume), and organization ID (“Org ID”) maybe used to set thresholds for eligibility, such as a minimum of $X MM inannual sales and a maximum of 0.x % chargeback rate. Such chargebackrate thresholding may be used as a proxy for e-commerce eligibilityand/or risk profile analysis. In another embodiment, step 255 mayinvolve “PIN prompting,” which reduces acceptance costs by shiftingsignature transactions to PIN debit transactions, and seamlessly routingsignature debit transactions to least-cost PIN-less debit networks.

FIG. 2B depicts a flow diagram of an exemplary method executed by atransaction routing server for routing electronic payment transactionsusing payment pseudo-networks and electronic transaction simulation andforecasting, in accordance with non-limiting embodiments. Specifically,FIG. 2B depicts a method 220 for routing electronic payment transactionsusing payment pseudo-networks and electronic transaction simulation,forecasting, and iteration. In one embodiment, method 220 includes step235, which may include receiving transaction-related information from aterminal. As illustrated in steps 240A-240F, the transaction-relatedinformation may include an issuing or “issuer” bank identificationnumber (“BIN”) 240A, an identification of the available payment networks(“payment network IDs”) 240B, an identification of one or more merchantcategories 240C, an identification of a regulatory status of the issuer(issuing bank) 240D, the transaction amount charged or to be charged240E, and a preferred (or non-preferred) status 240F associated with themerchant affiliated with the transaction. In some embodiments, forexample in transactions involving e-commerce, or an online purchase, thetransaction-related information need not include a terminal ID, e.g.,where a physical terminal does not exist. The modes and/or forms ofpayment may include, but are not limited to, the type of card presented,the specific information contained in the transaction, how and when apayment transaction is processed, the industry of the merchant, whetheradditional services (e.g., address verification service (AVS)) areutilized, etc.

Step 245 may include extracting routing criteria from thetransaction-related information, including but not limited to the BIN240A, available payment network IDs 240B, merchant categories 240C,issuer regulatory status 240D, transaction amount 240E, and preferredstatus 240F. In some embodiments, a default or initial payment networkmay be identified from the first digit of the payment card number and/ora bank card number (e.g., Visa, MasterCard, Discover, American Express,JCB, etc., for credit networks, and/or Star, Plus, Genie, Cirrus, etc.,for debit networks).

Step 250 may include dynamically identifying eligible networks based onextracted transaction routing criteria. For example, step 220 mayinclude identifying eligible networks based on the identity of thecardholder's issuing bank and/or the identity and/or category of therelevant merchant corresponding to the transaction.

Step 252 may include predicting a likelihood of authorization acceptancefor each identified network. For example, step 252 may include comparingone or more parameters of the payment transaction to the factor successdata. Such comparison may include applying factor weights from thefactor success data to the parameters of the transaction. Such factorweights may be determined by any suitable method, including, forexample, statistical methods including regression analysis, machinelearning, artificial intelligence methods such as neural networks, etc.

Step 255 may include generating pseudo-networks reflecting potentialalternative networks on which to route transaction. In some embodiments,pseudo-networks may be generated based on exempt vs. regulated statusand standard vs. preferred rates.

Step 260 may include generating one or more rate tables comprising asorting of eligible networks and generated pseudo-networks, andcorresponding routing and/or acceptance costs, the acceptance costsincluding costs associated with a low predicted likelihood ofauthorization acceptance.

Step 265 may include identifying or receiving negotiated volumediscounts and/or regulatory exemption thresholds. For example, in somecases, merchants, processors, and/or networks may negotiate preferredrates and/or volume discounts for given transaction amounts ortransaction volumes. Thus, step 265 may comprise receiving informationabout negotiated volume discounts and/or regulatory exemptionthresholds.

Step 270 may include executing simulation and forecasting models basedon routing transactions across the one or more generated rate tables,constrained by the identified or received negotiated volume discountsand/or regulatory exemption thresholds. Step 275 may include identifyinga lowest opportunity-cost network or pseudo-network based on iterativesimulation of routing through the simulation and forecasting models.

For example, as will be discussed with respect to FIG. 3 below, one ormore of an iterative algorithm 308, simulation module 304, andforecasting module 306 may be configured to interact to accuratelyforecast the absolute dollar amount of routing, and minimize theopportunity cost of pulling transactions from one network and routingthem through another alternative network (e.g., a createdpseudo-network), so as to reach negotiated volume discounts, comply withregulatory rules, leverage preferred rates, and reach other importanttechnical and business goals. As an example, it may be the case that agiven merchant falls short of a negotiated volume rate by $1M. In such acase, it may be worth sending additional transactions to that network ina non-least-cost manner, just in order to get that volume discount.

FIG. 3 depicts a schematic diagram 300 of a plurality of modules and/orsub-systems of the transaction routing server 116 of FIG. 1 , forrouting electronic payment transactions to PIN-less debit networks anddynamically routing electronic payment transactions using paymentpseudo-networks and electronic transaction simulation, in accordancewith non-limiting embodiments. Specifically, transaction routing server116 may include a pseudo-network creation module 302, a simulationmodule 304, a forecasting module 306, an iterative algorithm module 308,an opportunity cost calculator 310, and a parameter/threshold source 312for obtaining and disseminating, e.g., network options, issuerpreferences, volume rules/agreements, preferred rates, regulatory rules,etc. Simulation module 304 may include network table generator 314 andauthorization acceptance predictor 316. As shown in FIG. 3 , at a highlevel, parameter/threshold source 312 may obtain and disseminateparameters and thresholds, e.g., network options, issuer preferences,volume rules/agreements, preferred rates, regulatory rules, etc. to thepseudo-network creation module 302 and the opportunity cost calculator310. Results of simulation module 304 may be fed into forecasting module306 and iterative algorithm 308. Results of forecasting module 306 maybe fed into iterative algorithm 308 and back into simulation module 304.

In one embodiment, pseudo-network creation module 302 may be configuredto identify and generate new pseudo-networks to be included in ratetables and compared to existing payment networks in a dynamic routingalgorithm. In one embodiment, pseudo-network creation module 302 mayaccount for “standard” vs. “preferred” and card type, which enablescreation of a full and realistic table of the available rates. Moreover,pseudo-network creation module 302 may incorporate each of: signaturedebit networks, PIN debit networks, PIN-less debit networks, and creditnetworks (including chip-credit networks).

In one embodiment, simulation module 304 and forecasting module 306 mayinteract to simulate transaction routing over a period of time, such asover a day, week, month, quarter, year, and so on. Thus, network tablegenerator 314 may be configured to implement and analyze the generatedtables of networks and pseudo-networks, and determine what total dollarvalue thresholds are achieved, and so on. Authorization acceptancepredictor 316 may process the dataset of processing results to determinesuccess factors related to a likelihood of authorization or conversionfor subsequent transaction requests. Generating the factor success datamay include applying data science methods, machine learning, or othersuitable methods to the dataset of processing results. Forecastingmodule 306 may thereby predict year-over-year growth, and predict othertargets, thresholds, and metrics over time, using results of simulationmodule 304.

Iterative algorithm 308 may be configured to receive inputs fromsimulation module 304 and forecasting module 306, as well as opportunitycost calculator 310 to determine which transaction to run in anon-least-cost manner that will ultimately achieve better long-termgoals. For example, one or more of iterative algorithm 308, simulationmodule 304, and forecasting module 306 may be configured to interact toaccurately forecast the absolute dollar amount of routing, and minimizethe opportunity cost of pulling transactions from one network androuting them through another alternative network (e.g., a createdpseudo-network), so as to reach negotiated volume discounts, comply withregulatory rules, leverage preferred rates, and reach other importanttechnical and business goals. As an example, it may be the case that agiven merchant falls short of a negotiated volume rate by $1M. In such acase, it may be worth sending additional transactions to that network ina non-least-cost manner, just in order to get that volume discount.Iterative algorithm 308, simulation module 304, and forecasting module306 may be configured to determine which merchant's transactions and/orwhich other network's transactions to route in order to achieve thedesired volume discount.

FIG. 4A depicts a table of payment networks including createdpseudo-networks, in accordance with non-limiting embodiments.

FIG. 4B depicts a table of payment networks including createdpseudo-networks, sorted according to a simulated forecast of transactionvolume and rates, in accordance with non-limiting embodiments.

FIG. 5 depicts a screenshot of an exemplary user interface fordisplaying results of routing electronic payment transactions usingpayment pseudo-networks and electronic transaction simulation, andtransaction forecasting and iteration.

FIGS. 6A and 6B depict a report of anonymized PIN-less debit systemincluding a report 602 of value drivers of savings (FIG. 6A) and areport 604 of PIN-less network volume shift (FIG. 6B). Specifically,FIG. 6A depicts a graphical report 602 of value drivers of savingsincluding, for example, regulatory (e.g., Durbin) qualification, 40+tables/MCC differentiation, card/product type identification, andoverall savings potential. FIG. 6B depicts a graphical report 604reflecting a representation of PIN-less network volume shift by percent(%) of PIN-less eligible transactions across various networks, including“no option” MasterCardNisa (“MCNS”), “least cost” MasterCardNisa(“MCNS”), Accel, Jeanie, Maestro, Star, and so on. The graphical reportsof FIGS. 6A and 6B reflect that, given at least one sample transaction,as many as 99.94% of all signature debit transactions could satisfycurrent PIN-less eligibility requirements. Moreover, assuming least costpriority 24.3% of eligible signature debit transactions convertedPIN-less debit assuming certain PIN-less pricing.

FIG. 7 is a screenshot of an exemplary graphical user interface (GUI)depicting routing and settlement for an exemplary merchant, including adisplay of regulated vs. exempt issuer transactions and a distributionof transactions across eligible networks for the given transactioncriteria.

FIG. 8 is a screenshot of an exemplary graphical user interface (GUI)depicting an exemplary “issuer distribution,” e.g., of issuer, brand,and network detail, with selective user elements enabling sorting bycount, volume, state, issuer, network, etc. As shown in FIG. 8 , in oneembodiment, it a circular graphical display may depict the relevantdistributions by both state and percentage within a given issuer. FIG. 8shows that, in this case, e.g., 50.5% of Bank of America MasterCardtransactions in Florida were routed to Star (where the PIN was entered),and that these transactions represented 1.86% of total pin transactionsin Florida. This type of interactive visualization may enable merchants(clients of the routing server system) to drill-down into debit volumeby state, regulated status, issuer, brand, PIN debit network, and so on.

FIG. 9 is a screenshot of an exemplary graphical user interface (GUI)depicting an exemplary interface for reviewing and selecting analternate network eligibility. Specifically, the visualization of FIG. 9enables a merchant (client of the routing server system) to betterunderstand a maximum volume opportunity for a given PIN debit network.As shown in FIG. 9 , transactions are settled with the networks on thevertical axis, whereas the horizontal axis contains other eligiblenetworks for the transactions. In particular, as shown in FIG. 9 , inone embodiment, the disclosed interface may comprise different tabs forregulated vs. exempt issuers or networks. Each tab may comprise columnheaders indicating eligible networks of where transactions could besent, row headers indicating eligible networks where the transactionswere actually sent, and intersecting bubbles having sizes indicatingnumbers of transactions sent to one network that could have been sent toanother network, at a savings. Moreover, in one embodiment, mousingover, hovering over, or touch-tapping the vertical axis, the indicatedbubbles, or other locations, may expose raw and/or formatted data.

These and other embodiments of the systems and methods may be used aswould be recognized by those skilled in the art. The above descriptionsof various systems and methods are intended to illustrate specificexamples and describe certain ways of making and using the systemsdisclosed and described here. These descriptions are neither intended tobe nor should be taken as an exhaustive list of the possible ways inwhich these systems can be made and used. A number of modifications,including substitutions of systems between or among examples andvariations among combinations can be made. Those modifications andvariations should be apparent to those ordinarily skilled in this areaafter having read this disclosure.

It is intended that the specification and examples be considered asexemplary only, with a true scope and spirit of the invention beingindicated by the following claims. Other embodiments of the disclosurewill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. It isintended that the specification and examples be considered as exemplaryonly, with a true scope and spirit of the invention being indicated bythe following claims.

What is claimed is:
 1. A computer-implemented method for routingelectronic payment transactions to PIN-less networks using paymentpseudo-networks and electronic transaction simulation, the methodcomprising: predicting a likelihood of authorization acceptance for eachof a plurality of eligible payment networks based on transaction-relatedinformation from a merchant; and routing transactions from the merchantto a least cost payment network selected from the plurality of eligiblepayment networks based at least in part on costs associated with a lowpredicted likelihood of authorization acceptance.
 2. Thecomputer-implemented method of claim 1, wherein the transaction relatedinformation includes one or more of: a bank identification number(“BIN”); one or more available payment network IDs; one or more merchantcategories; an issuer regulatory status; a transaction amount; apreferred status; a primary account number; an issuer identificationnumber; a payment card number; a mode of payment including a swiping ofa card, keying in an identification related to the payment network, acontactless mode of payment, or a mode of payment that utilizes a chip);and whether an address verification system was utilized.
 3. Thecomputer-implemented method of claim 1, further comprising: identifyingone or more pseudo-networks corresponding to one or more of the eligiblenetworks, each pseudo-network comprising a modification of a network toaccount for a change in regulatory exemption status or a change inpreferred status.
 4. The computer-implemented method of claim 1, furthercomprising: dynamically sorting the eligible networks according to oneor more of breakeven transaction amounts, absolute cost, availability ofvolume discount, and availability of preferred rates.
 5. Thecomputer-implemented method of claim 1, further comprising: receiving anidentification of a regulatory threshold or negotiated volume threshold;identifying one or more pseudo-networks corresponding to one or more ofthe eligible networks, each pseudo-network comprising a modification ofa network to account for a change in regulatory exemption status or achange in preferred status; dynamically identifying one or morebreakeven transaction amounts for each eligible network, the breakeventransaction amounts defining a point at which two or more eligiblenetworks have the same expenses for a given transaction amount; anddynamically sorting the eligible networks and pseudo-networks toidentify a least-cost network.
 6. The computer-implemented method ofclaim 1, further comprising: generating a display of routing andsettlement across eligible networks, and user-configurable displays ofregulated vs. exempt issuers.
 7. The computer-implemented method ofclaim 1, wherein the transactions are signature debit transactions andthe least cost payment network is a PIN-less debit network.
 8. Thecomputer-implemented method of claim 7, further comprising: generating aplurality of user elements enabling a merchant user to reveal a group oftransaction routing decisions according to issuer, regulatory exemptionstatus, and preference or preferred rate status.
 9. A system for routingelectronic payment transactions to PIN-less networks using paymentpseudo-networks and electronic transaction simulation, the systemcomprising: a data storage device storing instructions for routingelectronic payment transactions to PIN-less networks using paymentpseudo-networks and electronic transaction simulation in an electronicstorage medium; and a processor configured to execute the instructionsto perform a method including: predicting a likelihood of authorizationacceptance for each of a plurality of eligible payment networks based ontransaction-related information from a merchant; and routing signaturedebit transactions from the merchant to a least cost PIN-less networkselected from the plurality of eligible networks based at least in parton costs associated with a low predicted likelihood of authorizationacceptance.
 10. The system of claim 9, wherein the transaction relatedinformation includes one or more of: a bank identification number(“BIN”); one or more available payment network IDs; one or more merchantcategories; an issuer regulatory status; a transaction amount; apreferred status; a primary account number; an issuer identificationnumber; a payment card number; a mode of payment including a swiping ofa card, keying in an identification related to the payment network, acontactless mode of payment, or a mode of payment that utilizes a chip);and whether an address verification system was utilized.
 11. The systemof claim 9, wherein the system is further configured for: identifyingone or more pseudo-networks corresponding to one or more of the eligiblenetworks, each pseudo-network comprising a modification of a network toaccount for a change in regulatory exemption status or a change inpreferred status.
 12. The system of claim 9, wherein the system isfurther configured for: dynamically sorting the eligible networksaccording to one or more of breakeven transaction amount, absolute cost,availability of volume discount, and availability of preferred rates.13. The system of claim 9, wherein the system is further configured for:receiving an identification of a regulatory threshold or negotiatedvolume threshold; identifying one or more pseudo-networks correspondingto one or more of the eligible networks, each pseudo-network comprisinga modification of a network to account for a change in regulatoryexemption status or a change in preferred status; dynamicallyidentifying one or more breakeven transaction amounts for each eligiblenetwork, the breakeven transaction amounts defining a point at which twoor more eligible networks have the same expenses for a given transactionamount; and dynamically sorting the eligible networks andpseudo-networks to identify a least-cost network.
 14. The system ofclaim 9, wherein the system is further configured for: generating adisplay of routing and settlement across eligible networks, anduser-configurable displays of regulated vs. exempt issuers.
 15. Thesystem of claim 9, wherein the transactions are signature debittransactions and the least cost payment network is a PIN-less debitnetwork.
 16. A non-transitory machine-readable medium storinginstructions that, when executed by a computing system, causes thecomputing system to perform a method for routing electronic paymenttransactions to PIN-less networks using payment pseudo-networks andelectronic transaction simulation, the method including: predicting alikelihood of authorization acceptance for each of a plurality ofeligible payment networks based on transaction-related information froma merchant; and routing signature debit transactions from the merchantto a least cost PIN-less network selected from the plurality of eligiblenetworks based at least in part on costs associated with a low predictedlikelihood of authorization acceptance.
 17. The non-transitorymachine-readable medium of claim 16, the method further comprising:identifying one or more pseudo-networks corresponding to one or more ofthe eligible networks, each pseudo-network comprising a modification ofa network to account for a change in regulatory exemption status or achange in preferred status.
 18. The non-transitory machine-readablemedium of claim 16, the method further comprising dynamically sortingthe eligible networks according to one or more of breakeven transactionamount, absolute cost, availability of volume discount, and availabilityof preferred rates.
 19. The non-transitory machine-readable medium ofclaim 16, the method further comprising: receiving an identification ofa regulatory threshold or negotiated volume threshold; identifying oneor more pseudo-networks corresponding to one or more of the eligiblenetworks, each pseudo-network comprising a modification of a network toaccount for a change in regulatory exemption status or a change inpreferred status; dynamically identifying one or more breakeventransaction amounts for each eligible network, the breakeven transactionamounts defining a point at which two or more eligible networks have thesame expenses for a given transaction amount; and dynamically sortingthe eligible networks and pseudo-networks to identify a least-costnetwork.
 20. The non-transitory machine-readable medium of claim 16,wherein the transactions are signature debit transactions and the leastcost payment network is a PIN-less debit network.